Task-Motion Planning for Navigation in Belief Space
Abstract
We present an integrated Task-Motion Planning (TMP) framework for navigation in large-scale environment. Autonomous robots operating in real world complex scenarios require planning in the discrete (task) space and the continuous (motion) space. In knowledge intensive domains, on the one hand, a robot has to reason at the highest-level, for example the regions to navigate to; on the other hand, the feasibility of the respective navigation tasks have to be checked at the execution level. This presents a need for motion-planning-aware task planners. We discuss a probabilistically complete approach that leverages this task-motion interaction for navigating in indoor domains, returning a plan that is optimal at the task-level. Furthermore, our framework is intended for motion planning under motion and sensing uncertainty, which is formally known as belief space planning. The underlying methodology is validated with a simulated office environment in Gazebo. In addition, we discuss the limitations and provide suggestions for improvements and future work.
Cite
@article{arxiv.1910.11683,
title = {Task-Motion Planning for Navigation in Belief Space},
author = {Antony Thomas and Fulvio Mastrogiovanni and Marco Baglietto},
journal= {arXiv preprint arXiv:1910.11683},
year = {2019}
}
Comments
Accepted for publication in the proceedings of the International Symposium on Robotics Research (ISRR) 2019. arXiv admin note: text overlap with arXiv:1908.10227